2017
DOI: 10.1016/j.asoc.2017.03.012
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A multi-objective and evolutionary hyper-heuristic applied to the Integration and Test Order Problem

Abstract: The field of Search-Based Software Engineering (SBSE) has widely utilized Multi-Objective Evolutionary Algorithms (MOEAs) to solve complex software engineering problems. However, the use of such algorithms can be a hard task for the software engineer, mainly due to the significant range of parameter and algorithm choices. To help in this task, the use of Hyper-heuristics is recommended. Hyper-heuristics can select or generate low-level heuristics while optimization algorithms are executed, and thus can be gene… Show more

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Cited by 35 publications
(31 citation statements)
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“…For example, in an object-oriented system units are classes, in component-based programming units are components, in aspectoriented programming units are aspects, and in product line oriented systems units may be considered product features [2]. These characteristics make this problem suitable for the application of meta-and hyper-heuristics, since these kinds of algorithms are capable of optimizing several objectives at once [3], and are capable of being easily applied to different contexts without needing to have their implementation adapted for this end [7].…”
Section: Problem Description and Solution Methodologymentioning
confidence: 99%
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“…For example, in an object-oriented system units are classes, in component-based programming units are components, in aspectoriented programming units are aspects, and in product line oriented systems units may be considered product features [2]. These characteristics make this problem suitable for the application of meta-and hyper-heuristics, since these kinds of algorithms are capable of optimizing several objectives at once [3], and are capable of being easily applied to different contexts without needing to have their implementation adapted for this end [7].…”
Section: Problem Description and Solution Methodologymentioning
confidence: 99%
“…A further performance comparison of using HITO within an SPEA2 framework was given in a later paper [6]. Guizzo et al [7] provided another extension, formulating the problem as a many-objective problem with four objectives, comparing to a number of state-of-the-art MOEAs.…”
Section: Introductionmentioning
confidence: 99%
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“…They integrated of the HH component, credit assignment, and selection method (i.e., random and upper confidence bound) into MOEAs, that is, NSGA-II, SPEA2, indicator-based evolutionary algorithm (IBEA), and MOEA based on decomposition (MOEA/D). Guizzo et al [56] designed their MOHH to tackle integration and test ordering in Google Guava by inserting it MOHH into the MOEAs framework to evaluate the operators' performance through CF and MAB. Yao et al [57] proposed a MOHH framework for walking route planning in a smart city and a reinforcement learning mechanism was established to select the LLH.…”
Section: Hyper-heuristic Reviewmentioning
confidence: 99%
“…The problem sets contained different numbers of previously mentioned systems for the ITO problem. For each system, the stopping criterion was set to 60,000 fitness evaluations [9,29] The number of repetitions in MOCRS-Tuning was set to 8. The control parameters which underwent the tuning process were population size, crossover probability, and mutation probability.…”
Section: Experimental Settingsmentioning
confidence: 99%